利用遥感和人工智能绘制城市固体废物填埋场的热足迹图

IF 2.3 Q2 REMOTE SENSING
Nawras Shatnawi, Munjed Al-Sharif, Majd A. Briezat
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引用次数: 0

摘要

这项工作展示了将遥感、回归模型、随机森林(RF)算法和人工神经网络(ANN)相结合,为约旦垃圾填埋场管理提供重要信息的价值。利用线性和非线性回归模型、ANN 和 RF 预测陆地表面温度(LST)的过程依赖于从大地遥感卫星图像中检索到的 2000 年至 2018 年陆地表面温度时间序列。此外,研究还利用了归一化差异植被指数(NDVI)、归一化差异水分指数(NDMI)以及湿度、风速和环境空气温度数据。部署的 ANN 模型的判定系数为 0.87,平均平方误差为 6.40*10^-8。同样,射频模型准确识别了 93.88% 的 LST 值。研究结果表明,垃圾填埋场的 LST 始终高于夏季气温,开放式垃圾填埋场单元的 LST 超过封闭式单元的 LST。此外,ANN 和 RF 模型预测的 LST 值超过了线性和非线性回归模型。值得注意的是,R^2 值为 0.81,表明 ANN 和 RF 的研究结果之间具有很强的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping the thermal footprint of a municipal solid waste landfill using remote sensing and artificial intelligence

This work demonstrates the value of combining remote sensing, regression models, random forest (RF) algorithms, and artificial neural networks (ANN) to provide crucial information for landfill management in Jordan. The process of predicting land surface temperature (LST) using linear and nonlinear regression models, ANN, and RF depended on past LST time series retrieved from Landsat images for the years 2000 to 2018. Additionally, the study utilized the normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), as well as data on humidity, wind velocity, and ambient air temperature. The deployed ANN model exhibited a coefficient of determination of 0.87 and a mean squared error of 6.40*10^-8. Similarly, the RF model accurately identified 93.88% of the LST values. The findings revealed that the LST at landfills was consistently higher than the summer air temperature, and that the LSTs of open landfill cells exceeded those of closed cells. Moreover, the predicted LST values from ANN and RF models surpassed those from linear and nonlinear regression models. Notably, the R^2 value of 0.81 indicates a strong correlation between ANN and RF findings.

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来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
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